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Multi-objective optimization of genome-scale metabolic models: the case of ethanol production

Author

Listed:
  • Andrea Patané

    (University of Oxford)

  • Giorgio Jansen

    (University of Catania
    University of Cambridge)

  • Piero Conca

    (CNR)

  • Giovanni Carapezza

    (Nerviano Medical Sciences)

  • Jole Costanza

    (Italian Institute of Technology - IIT)

  • Giuseppe Nicosia

    (University of Catania
    University of Cambridge
    University of Reading)

Abstract

Ethanol is among the largest fermentation product used worldwide, accounting for more than $$90\%$$ 90 % of all biofuel produced in the last decade. However current production methods of ethanol are unable to meet the requirements of increasing global demand, because of low yields on glucose sources. In this work, we present an in silico multi-objective optimization and analyses of eight genome-scale metabolic networks for the overproduction of ethanol within the engineered cell. We introduce MOME (multi-objective metabolic engineering) algorithm, that models both gene knockouts and enzymes up and down regulation using the Redirector framework. In a multi-step approach, MOME tackles the multi-objective optimization of biomass and ethanol production in the engineered strain; and performs genetic design and clustering analyses on the optimization results. We find in silico E. coli Pareto optimal strains with a knockout cost of 14 characterized by an ethanol production up to $$ 19.74 \, \hbox {mmol} \, \hbox {gDW}^{-1} \, \hbox {h}^{-1} $$ 19.74 mmol gDW - 1 h - 1 ( $$+\,832.88 \% $$ + 832.88 % with respect to wild-type) and biomass production of $$0.02 \, \hbox {h}^{-1}$$ 0.02 h - 1 ( $$-\,98.06 \% $$ - 98.06 % ). The analyses on E. coli highlighted a single knockout strategy producing $$16.49 \, \hbox {mmol} \, \hbox {gDW}^{-1} \, \hbox {h}^{-1} $$ 16.49 mmol gDW - 1 h - 1 ( $$+\,679.29 \%$$ + 679.29 % ) ethanol, with biomass equals to $$0.23 \, \hbox {h}^{-1}$$ 0.23 h - 1 ( $$-\,77.45 \% $$ - 77.45 % ). We also discuss results obtained by applying MOME to metabolic models of: (i) S. aureus; (ii) S. enterica; (iii) Y. pestis; (iv) S. cerevisiae; (v) C. reinhardtii; (vi) Y. lipolytica. We finally present a set of simulations in which constrains over essential genes and minimum allowable biomass were included. A bound over the maximum allowable biomass was also added, along with other settings representing rich media compositions. In the same conditions the maximum improvement in ethanol production is $$+\,195.24\%$$ + 195.24 % .

Suggested Citation

  • Andrea Patané & Giorgio Jansen & Piero Conca & Giovanni Carapezza & Jole Costanza & Giuseppe Nicosia, 2019. "Multi-objective optimization of genome-scale metabolic models: the case of ethanol production," Annals of Operations Research, Springer, vol. 276(1), pages 211-227, May.
  • Handle: RePEc:spr:annopr:v:276:y:2019:i:1:d:10.1007_s10479-018-2865-4
    DOI: 10.1007/s10479-018-2865-4
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    References listed on IDEAS

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    1. Gupta, Anubhuti & Verma, Jay Prakash, 2015. "Sustainable bio-ethanol production from agro-residues: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 550-567.
    2. Grazziela P Figueredo & Peer-Olaf Siebers & Markus R Owen & Jenna Reps & Uwe Aickelin, 2014. "Comparing Stochastic Differential Equations and Agent-Based Modelling and Simulation for Early-Stage Cancer," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-18, April.
    3. Graham Rockwell & Nicholas J Guido & George M Church, 2013. "Redirector: Designing Cell Factories by Reconstructing the Metabolic Objective," PLOS Computational Biology, Public Library of Science, vol. 9(1), pages 1-15, January.
    4. Balat, Mustafa & Balat, Havva, 2009. "Recent trends in global production and utilization of bio-ethanol fuel," Applied Energy, Elsevier, vol. 86(11), pages 2273-2282, November.
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